Instructions to use VladHong/llama-cpp-K2-FAST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use VladHong/llama-cpp-K2-FAST2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,840 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | <script lang="ts">
import { Input } from '$lib/components/ui/input';
import { Label } from '$lib/components/ui/label';
import { fly } from 'svelte/transition';
interface Props {
name: string;
value: string;
suggestions?: string[];
isLoadingSuggestions?: boolean;
isAutocompleteActive?: boolean;
autocompleteIndex?: number;
onInput: (value: string) => void;
onKeydown: (event: KeyboardEvent) => void;
onBlur: () => void;
onFocus: () => void;
onSelectSuggestion: (value: string) => void;
}
let {
autocompleteIndex = 0,
isAutocompleteActive = false,
isLoadingSuggestions = false,
name,
onBlur,
onFocus,
onInput,
onKeydown,
onSelectSuggestion,
suggestions = [],
value = ''
}: Props = $props();
</script>
<div class="relative grid gap-1">
<Label class="mb-1 text-muted-foreground" for="tpl-arg-{name}">
<span>
{name}
<span class="text-destructive">*</span>
</span>
{#if isLoadingSuggestions}
<span class="text-xs text-muted-foreground/50">...</span>
{/if}
</Label>
<Input
autocomplete="off"
id="tpl-arg-{name}"
onblur={onBlur}
onfocus={onFocus}
oninput={(e) => onInput(e.currentTarget.value)}
onkeydown={onKeydown}
placeholder="Enter {name}"
type="text"
{value}
/>
{#if isAutocompleteActive && suggestions.length > 0}
<div
transition:fly={{ duration: 100, y: -5 }}
class="absolute top-full right-0 left-0 z-10 mt-1 max-h-32 overflow-y-auto rounded-lg border border-border/50 bg-background shadow-lg"
>
{#each suggestions as suggestion, i (suggestion)}
<button
class="w-full px-3 py-1.5 text-left text-sm hover:bg-accent {i === autocompleteIndex
? 'bg-accent'
: ''}"
onmousedown={() => onSelectSuggestion(suggestion)}
type="button"
>
{suggestion}
</button>
{/each}
</div>
{/if}
</div>
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